Deep Learning for Fast Simulation
- 1. 1 Deep Learning for Fast Simulation HNSciCloud M-PIL-3.2 meeting June 2018 S. Vallecorsa F.Carminati G. Khattak
- 2. 2 Our objective • Activities on-going to speedup Monte Carlo techniques • Not enough to cope with HL-LHC expected needs • Current fast simulation solutions are detector dependent • A general fast simulation tool based on Machine Learning/Deep Learning • Optimizing training time becomes crucial Improved, efficient and accurate fast simulation 2
- 3. 3 Requirements Precise simulation results Detailed validation process A fast inference step Generic customizable tool Easy-to-use and easily extensible framework Large hyper-parameters scans and meta-optimisation: Training time under control Scalability Possibility to work across platforms 3
- 4. 4 Generator G generates data from random noise Discriminator D learns how to distinguish real data from generated data 4 Simultaneously train two networks that compete and cooperate with each other Generative adversarial networks arXiv:1406.2661v1 Image source: The (blind) counterfeiter/detective case Counterfeiter shows the Monalisa Detective says it is fake and gives feedback Counterfeiter makes new Monalisa based on feedback Iterate until detective is fooled https://arxiv.org/pdf/1701.00160v1.pdf
- 5. 5 Generated images Interpret detector output as a 3D image 5 GAN generated electron shower Y moment (width) Average shower section 3D convolutional GAN generate realistic detector output Customized architecture (includes auxiliary regression tasks) Agreement to standard Monte Carlo in terms of physics is remarkable! Energy fraction measured by the calorimeter on Caltech ibanks GPU cluster thanks to Prof M. Spiropulu
- 6. 6 Distributed training is needed Inference: Monte Carlo: 17 s/particle vs 3DGAN: 7 ms/particle è speedup factor > 2500 on CPU!! Training: 45 min/epoch on a NVIDIA P100 Introduce data parallel training using mpi-learn (Elastic Averaging Stochastic Gradient Descent) Computing performance Calorimeter energy response: GAN prediction stays stable through 20 nodes! Strong scaling measured at CSCS Swiss National Super Computing Center (J-R. Vlimant) Time to create an electron shower Method Machine Time/Shower (msec) Full Simulation (geant4) Intel Xeon Platinum 8180 17000 3d GAN (batch size 128) Intel Xeon Platinum 8180 7 3d GAN (batchsize 128) P100 0.04
- 7. 7 DL with the HNSciCloud First tests during prototype (2017) Single GPU training benchmark ( RHEA, T-Systems, IBM) P100 (RHEA - Exoscale) vs K80 (IBM) Current tests MPI based distributed training (ssh/TCP) Local input storage Single GPU per node Comparison to HPC environment Trials with HTCondor on Exoscale cloud (5 VMs) (still under investigation) 2 2 P100 T-Systems (CSCS)
- 8. 8 Next steps Continue with tests/optimisation: • Schedulers (SLURM) • Input storage options • GPU/node configuration • Possibility to combine GPUs from different resources Additional GPUs are needed First results are very promising 8